High performance, computationally efficient real-time dynamic controller software and hardware are required for use in complex multi-input, multi-output, nonlinear, time-varying systems that are operating in challenging environments. In particular, there is a need for efficient and reliable controllers for dynamic systems having large numbers of system inputs and outputs. The need for robust, adaptive controllers arises from the fact that systems engineers always encounter modeling uncertainties and unmeasurable external perturbations when designing control systems. It is generally impossible to describe the dynamics of most real systems by precise mathematical models. Although very good system models can be obtained for some devices in artificial laboratory environments, accurate modeling is unrealistic in complex and challenging environments.
Existing adaptive controllers such as the adaptive Pole Placement controller and the Minimum Variance controllers are promising approaches to accomplish these control tasks. However, they suffer from two potentially crippling limitations: (1) computational complexity, which limits their feasibility in multivariable applications; and (2) sensitivity to the choice of the input-output delays and model order selection. There has been considerable research recently in the development of adaptive controllers that attempt to overcome these limitations. A major focus has been the development of extended horizon predictive control methods.